Data Science and AI Specialist in Glasgow

Data Science and AI Specialist – University of Glasgow

Posted 20 June 2025 | Salary Grade 7: £41,064 – £46,049 per annum | End date 08 January 2026 | Location: Glasgow | Full‑time (35 hours per week) | Funding for up to 3 years initially.

Job Purpose

To provide advanced analytical, epidemiological, and data‑science support for research projects using NHS data hosted within the Trusted Research Environment (TRE). The postholder will work closely with investigators from NHS Greater Glasgow and Clyde (NHSGGC), the University of Glasgow (UofG), and industry partners to translate research ideas into robust analytical plans, ensure data are appropriately specified and prepared for analysis, and deliver high‑quality, reproducible outputs. The role focuses on real‑world health data analysis—including study design, data wrangling, phenotype development, data integration and statistical and machine‑learning methods—to accelerate project delivery, strengthen grant applications and advance the overall research capability of the TRE.

Main Duties and Responsibilities

• Support principal investigators by designing and implementing robust analytical and statistical workflows for complex clinical and population health datasets hosted in the TRE—including data wrangling, quality assessment, phenotype development and exploratory analyses.
• Develop reproducible and transparent analytical pipelines, ensuring data provenance, version control and adherence to ethical and governance standards.
• Work closely with clinicians, researchers and data engineers across NHS and UofG to define project data requirements, optimise analytical design and translate research questions into executable analyses.
• Lead on technical aspects of data integration, statistical and machine‑learning model development, validation, interpretability and deployment within the secure TRE environment.
• Ensure all research activities comply with NHS data governance, ISO standards and the TRE’s ethical frameworks.
• Contribute to demonstration and exemplar projects (e.g., multimodal data integration, digital phenotyping, predictive analytics) that highlight the TRE’s analytical and AI capabilities.
• Act as liaison between NHS Safe Haven, academic researchers and University Services (e.g., Information Services, Centre for Data Science and AI) advising on data specifications, study design and appropriate analytical methodologies.
• Support the training and mentoring of researchers and students in applied health data science, statistical methods and TRE workflows.
• Perform administrative and governance‑related tasks relevant to TRE operations, including documentation, data access tracking and project coordination.
• Keep up to date with current knowledge and recent advances in the field/discipline.
• Contribute to research outputs, grant applications and dissemination activities that strengthen TRE capabilities and support collaborative funding bids.
• Participate and engage with national and cross‑institutional AI/TRE initiatives and networks as appropriate.
• Undertake any other reasonable duties as required by the Head of School / Director of Clinical TRE.
• Contribute to the enhancement of the University’s international profile in line with the University Strategy.

Knowledge, Qualifications, Skills and Experience

Essential

• A1: Scottish Credit and Qualification Framework level 12 (PhD) in a relevant discipline such as Epidemiology, Biostatistics, Health Data Science or Health Informatics.
• A2: Strong knowledge of epidemiological and biostatistical principles applied to healthcare data, with experience integrating these with data‑science or AI/ML methods.
• A3: Demonstrable understanding of data governance and regulatory requirements for clinical data, including anonymisation, secure data handling protocols and workflows underpinning Trusted Research Environments (TREs).
• A4: Understanding of study design, phenotype development and data quality assessment in real‑world healthcare research.
• C1: Proficiency in R and/or Python, with strong skills in health data wrangling, cleaning, integration and visualisation; experience with analytical and machine‑learning frameworks (e.g., TensorFlow, PyTorch, Scikit‑learn).
• C2: Ability to manipulate, analyse and interpret large or complex healthcare datasets within secure computing environments, ensuring reproducibility and integrity.
• C3: Excellent communication and interpersonal skills to work across interdisciplinary teams in both academic and clinical environments.
• C4: Proven ability to explain analytical findings and complex technical concepts to non‑specialist stakeholders, including clinicians, policymakers and industry partners.
• C5: Problem‑solving mindset with the ability to work independently and manage multiple priorities.
• E1: Significant experience in applied health data analysis—including study design, data specification, data wrangling, statistical analysis and, where appropriate, machine‑learning model development or evaluation.
• E2: Experience working with sensitive health or clinical datasets within secure research environments or safe havens.
• E3: Experience contributing to research publications, technical reports or grant‑funded projects through provision of analytical and methodological expertise.
• E4: Experience working within data governance and ethical frameworks, ideally in healthcare or public sector research.
• E5: Proven commitment to supporting the career development of colleagues and to other forms of collegiality appropriate to the career stage.

Desirable

• B1: Additional formal training or certification in Epidemiology, Biostatistics, Health Informatics or Applied AI in Healthcare.
• B2: Knowledge of data standards and interoperability frameworks (e.g., OMOP, FHIR, SNOMED CT, ICD‑10) relevant to real‑world data integration.
• B3: Understanding of computable phenotypes, data harmonisation or ontology development for clinical research.
• B4: Awareness of federated analytics, privacy‑preserving computation or distributed learning within Trusted Research Environments.
• D1: Experience in developing reproducible analysis pipelines using tools such as Git, Docker or workflow managers.
• D2: Strong skills in data visualisation and dashboarding (e.g., R Shiny, Plotly, Dash, Power BI) for communicating insights to clinical and policy audiences.
• D3: Familiarity with advanced analytical techniques, such as causal inference, predictive modelling or survival analysis in health data contexts.
• F1: Prior experience supporting Safe Haven/TRE governance committees, data access processes or technical advisory groups.
• F2: Contribution to open‑source tools, data models or methods for healthcare analytics or AI reproducibility.
• F3: Experience in preparing grant applications or preliminary data analyses that directly supported successful research funding.
• F4: Evidence of continuous professional development in health data science, AI ethics or digital health innovation.

Terms and Conditions

• Salary Grade 7: £41,064 – £46,049 per annum.
• This post is full‑time (35 hours per week) and has funding for up to 3 years initially.
• Relocation assistance will be provided where appropriate.
• Previous applicants should not re‑apply for this position.

Benefits

1. A warm welcoming and engaging organisational culture, where your talents are developed and nurtured and success is celebrated and shared.
2. An excellent employment package with generous terms, including 41 days of leave for full‑time staff, pension and benefits/discount packages.
3. A flexible approach to working.
4. A commitment to support your health and wellbeing, including a free 6‑month UofG Sport membership for all new staff joining the University.

Equality and Diversity

We believe that we can only reach our full potential through the talents of all. Equality, diversity and inclusion are at the heart of our values. Applications are particularly welcome from across our communities and especially from the Black, Asian and Minority Ethnic (BAME) community and other under‑represented protected characteristics within the University. We endorse the principles of Athena Swan and hold bronze, silver and gold awards across the University.

Closing Date

08 January 2026 at 23:45